[jira] [Assigned] (SPARK-22291) Postgresql UUID[] to Cassandra: Conversion Error

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-22291?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-22291:
------------------------------------

    Assignee:     (was: Apache Spark)

> Postgresql UUID[] to Cassandra: Conversion Error
> ------------------------------------------------
>
>                 Key: SPARK-22291
>                 URL: https://issues.apache.org/jira/browse/SPARK-22291
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core, SQL
>    Affects Versions: 2.2.0
>         Environment: Debian Linux, Scala 2.11, Spark 2.2.0, PostgreSQL 9.6, Cassandra 3
>            Reporter: Fabio J. Walter
>              Labels: patch, postgresql, sql
>         Attachments: org_apache_spark_sql_execution_datasources_jdbc_JdbcUtil.png
>
>
> My job reads data from a PostgreSQL table that contains columns of user_ids uuid[] type, so that I'm getting the error above when I'm trying to save data on Cassandra.
> However, the creation of this same table on Cassandra works fine!  user_ids list<text>.
> I can't change the type on the source table, because I'm reading data from a legacy system.
> I've been looking at point printed on log, on class org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils.scala
> Stacktrace on Spark:
> {noformat}
> Caused by: java.lang.ClassCastException: [Ljava.util.UUID; cannot be cast to [Ljava.lang.String;
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$14.apply(JdbcUtils.scala:443)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$14.apply(JdbcUtils.scala:442)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$13$$anonfun$18.apply(JdbcUtils.scala:472)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$13$$anonfun$18.apply(JdbcUtils.scala:472)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$nullSafeConvert(JdbcUtils.scala:482)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$13.apply(JdbcUtils.scala:470)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$13.apply(JdbcUtils.scala:469)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:330)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:312)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
> at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
> at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1.hasNext(InMemoryRelation.scala:133)
> at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)
> at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1038)
> at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1029)
> at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:969)
> at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1029)
> at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:760)
> at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:285)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:108)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:748)
> {noformat}
> Proposed solution:
> At this specific point spark-sql_2.11-2.2.0-sources.jar!/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala:443
> {code:scala}
> //My suggestion is change the line 443 from
> ```array.asInstanceOf[Array[java.lang.String]]
>               .map(UTF8String.fromString)```
> //to 
> ```array.map(UTF8String.fromString(_.toString))```
> {code}



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